FKIE_CVE-2022-45060
Vulnerability from fkie_nvd - Published: 2022-11-09 06:15 - Updated: 2026-06-17 05:09
Severity
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N
7.5 (High) - CVSS:3.1/
Summary
An HTTP Request Forgery issue was discovered in Varnish Cache 5.x and 6.x before 6.0.11, 7.x before 7.1.2, and 7.2.x before 7.2.1. An attacker may introduce characters through HTTP/2 pseudo-headers that are invalid in the context of an HTTP/1 request line, causing the Varnish server to produce invalid HTTP/1 requests to the backend. This could, in turn, be used to exploit vulnerabilities in a server behind the Varnish server. Note: the 6.0.x LTS series (before 6.0.11) is affected.
References
Impacted products
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],
"url": "https://lists.debian.org/debian-lts-announce/2022/11/msg00036.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/G6ZMOZVBLZXHEV5VRW4I4SOWLQEK5OF5/"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/M4KVVCIQVINQQ2D7ORNARSYALMJUMP3I/"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/XGF6LFTHXCSYMYUX5HLMVXQH3WHCSFLU/"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Mitigation",
"Vendor Advisory"
],
"url": "https://varnish-cache.org/security/VSV00011.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Third Party Advisory"
],
"url": "https://www.debian.org/security/2023/dsa-5334"
}
],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "NVD-CWE-noinfo"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-20"
}
],
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"type": "Secondary"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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